They also adopted sophisticated plan-based dialogue models as well at the initial stage of the project.

the trend changed rather drastically in the early 90s and most research groups with practical applications in mind gave up such strategies and switched to more corpus-oriented and statistical methods.


Since the grammar overgenerates, we h a ve to choose single parse results among a combinatorially large numb e r o f p o ssible parses.

an experiment shows that a statistic method using ME we use the program for ME developed by NYU can select around 88.6 of correct analysis in terms of dependency relationships among !


XHPSG fails to produce parses for about half of the sentences that cover the whole.

in application such as IE, a system needs not have parses covering the whole sentence.


This apparent progress in spoken language technology has been fuelled by a n umber of developments: the relentless increase in desktop computing power, the introduction of statistical modelling techniques, the availability of vast quantities of recorded speech material, and the institution of public system evaluations.

our understanding of the fundamental patterning in speech has progressed at a much slower pace, not least in the area of its high-level linguistic properties.


The print data was much cleaner than the transcribed broadcast data in the sense that there were very few typographical errors, spelling and grammar were good.

the print data also had longer, more complex sentences with somewhat greater variety in the words used to represent dates.


They obtained roughly .91 Precision and .80 Recall on one test set, and .87 Precision and .68 Recall on another.

they adjust the reference time during processing, which is something that we have not yet addressed.


(The MUC task required recognizing a wider variety of TIMEXs, including event-dependent ones.

at least 30% of the dates and times in the MUC test were fixed-format ones occurring in document headers, trailers, and copyright notices. )


To take an example from the BNC, the adjectives large and green never occur together in the training data, and so would be assigned a random order by the direct evidence method.

the pairs large, new and new, green occur fairly frequently.


The two highest scoring methods, using memorybased learning and positional probability, perform similarly, and from the point of view of accuracy there is little to recommend one method over the other.

it is interesting to note that the errors made by the two methods do not completely overlap: while either of the methods gives the right answer for about 89% of the test data, one of the two is right 95.00% of the time.


Spoken dialogue managers have benefited from using stochastic planners such as Markov Decision Processes (MDPs).

so far, MDPs do not handle well noisy and ambiguous speech utterances.


There are several POMDP algorithms that may be the natural choice for policy generation (Sondik, 1971;Monahan, 1982;Parr and Russell, 1995;Cassandra et al., 1997;Kaelbling et al., 1998;Thrun, 1999).

solving real world dialogue scenarios is computationally in-tractable for full-blown POMDP solvers, as the complexity is doubly exponential in the number of states.


Most other actions are penalised with an equivalent negative amount.

the confir-mation/clarification actions are penalised lightly (values close to 0), and the motion commands are penalised heavily if taken from the wrong state, to illustrate the difference between an undesirable action that is merely irritating (i.e., giving an inappropriate response) and an action that can be much more costly (e.g., having the robot leave the room at the wrong time, or travel to the wrong destination).


Consequently, these algorithms collect training data for the and performance is not affected.

unsupervised methods break down on such examples.


evaluations based on judgements along these dimensions are clearly weaker than evaluations measuring actual behavioural and attitudinal changes (Olso and Zanna 1991).


The experiment results show that arguments generated at the more concise level are significantly better than arguments generated at the more verbose level.

further experiments are needed to determine what is the optimal level of conciseness.


Electronically readable Arabic text has only recently become available on a useful scale, hence our experiments were run on short texts.

the coverage of the data sets allows us to verify our experiments on demanding samples, and their size lets us verify correct clustering manually.


Finer grained evaluation of cluster quality would be needed in an IR context.

our main concern is comparing algorithms.


Unfortunately, this approach is not applicable to multivariate data with more than two dimensions.

we consider syllables to consist of at least three dimensions corresponding to parts of the internal syllable structure: onset, nucleus and coda.


For many applications, people have devoted considerable energy to improving both components, with resulting improvements in overall system accuracy.

relatively little research has gone into improving the channel model for spelling correction.


For instance, P(e | a) does not vary greatly between the three positions mentioned above.

p(ent | ant) is highly dependent upon position.


Problems with evaluation: Some of the statistics that we presented in the previous discussion suggest that this relatively simple statistical summarization system is not very good compared to some of the extraction based summarization systems that have been presented elsewhere (e.g., (Radev and Mani, 1997)).

it is worth emphasizing that many of the headlines generated by the system were quite good, but were penalized because our evaluation metric was based on the word-error rate and the generated headline terms did not exactly match the original ones.


Pereira's (Pereira, 1985) algorithm also stores changes to nodes separate from the graph.

pereira's mechanism incurs a log(n) overhead for accessing the changes (where n is the number of nodes in a graph), resulting in an O(n log n) time algorithm.


Every solution of a constraint is a solution of one of its irredundant solved forms.

the number of irredundant solved forms is always finite, whereas the number of solutions typically is not: X:a ∧ Y :b is in solved form, but each solution must contain an additional node with arbitrary label that combines X and Y into a tree (e.g.


Melamed (1999) aligns texts using correspondence points taken either from orthographic cognates (Michel Simard et al., 1992) or from a seed translation lexicon.

although the heuristics both approaches use to filter noisy points may be intuitively quite acceptable, they are not theoretically supported by Statistics.


In cases where TAG models dependencies correctly, the use of R-MCTAG is straightforward: when an auxiliary tree adjoins at a site pair which is just a single node, it looks just like conventional adjunction.

in problematic cases we can use the extra expressive p o wer of R-MCTAG to model dependencies correctly.


DSG can generate the language count-k for some arbitrary k that is, fa 1 n a 2 n : : : a k n g, which makes it extremely powerful, whereas R-MCTAG can only generate count-4.

dSG cannot generate the copy language that is, fww j w 2 g with some terminal al-phabet, whereas R-MCTAG can; this may be problematic for a formalism modeling natural language, given the key role of the copy language in demonstrating that natural language is not context-free Shieber, 1985.


E ectively, a dependency structure is made parasitic on the phrase structure so that they can be generated together by a context-free model.


Clearly, a direct comparison of these results to stateof-the-art statistical parsers cannot bemade because of di erent training and test data and other evaluation measures.

we w ould like to draw the following conclusions from our experiments: The problem of chaotic convergence behaviour of EM estimation can besolved for log-linear models.


For data consisting of unannotated sentences so-called incomplete data the iterative method of the EM algorithm (Dempster et al., 1977) has to be employed.

since even complete-data estimation for log-linear models requires iterative methods, an application of EM to log-linear models results in an algorithm which is expensive since it is doubly-iterative.


Thus a precise indication of correct c/f-structure pairs was possible.

the average ambiguity of this corpus is only 5.4 parses per sentence, for sentences with on average 7.5 words.


In recent years, statistical approaches on ATR (Automatic Term Recognition) (Bourigault, 1992;Dagan et al, 1994;Justeson and Katz, 1995;Frantzi, 1999) have achieved good results.

there are scopes to improve the performance in extracting terms still further.


Accuracy is somewhat similar to the familiar metric of precision in that it is calculated over cases for which a decision is made, and performance is similar to recall in that it is calculated over all true frame elements.

unlike a traditional precision recall trade-o , these results have no threshold to adjust, and the task is a multi-way classi cation rather than a binary decision.


It would seem reasonable to think that resolution of pronominal anaphora would only be accomplished when the ratio of pronominal occurrence exceeds a minimum level.

we have to take into account that the cost of solving these references is proportional to the number of pronouns analysed and consequently, proportional to the amount of information a system will ignore if these references are not solved.


Using this approach, we have developed grammar that understands declarative sentences (Shavitri, 1999).

our experience shows that we need to have a more detailed word categories than is currently available in the standard Indonesian word dictionary (KBBI) before the grammar can be used effectively.


The status of practical research on continuous speech recognition is in its initial step with at least one published paper [15].

to western speech recognition, topics specifying tonal languages or tone recognition have been deeply researched as seen in many papers e.g., [16].


Thus, SCFGs have been successfully used on limited-domain tasks of low perplexity.

sCFGs work poorly for large vocabulary, generalpurpose tasks, because the parameter learning and the computation of word transition probabilities present serious problems for complex real tasks.


The initial probabilities were randomly generated and three different seeds were tested.

only one of them is here given that the results were very similar.


At the endpoint of each training run in the graph, the same number of samples has been annotated for training.

we see that the larger the pool of candidate instances for annotation is, the better the resulting accuracy.


Variable occurrences or more generally strings in £ i 7 x p x @ r © 8 s can be instantiated to ranges.

an occurrence of the terminal y can be instantiated to the range .


With a wide coverage English TAG, on a small sample set of short sentences, a guided parser is on the average three times faster than its non-guided counterpart, while, for longer sentences, more than one order of magnitude may be expected.

the guided parser speed is very sensitive to the level of the guide, which must be chosen very carefully since potential benefits may be overcome by the time taken by the guiding structure book-keeping procedures.


The global parse time for TAGs might also be further improved using the transformation described in (Boullier, 1999) which, starting from any TAG, constructs an equivalent RCG that can be parsed in .

this improvement is not definite, since, on typical input sentences, the increase in size of the resulting grammar may well ruin the expected practical benefits, as in the case of the ¥ » -guiding parser processing short sentences.


A novel feature of our approach is the ability to extract multiple kinds of paraphrases: Identification of lexical paraphrases.

to earlier work on similarity, our approach allows identification of multi-word paraphrases, in addition to single words, a challenging issue for corpus-based techniques.


Statistical techniques were also successfully used by (Lapata, 2001) to identify paraphrases of adjective-noun phrases.

our method is not limited to a particular paraphrase type.


This is, of course, not exactly what the original query meant.

it is superior to queries like ''browsers'' AND NOT ''netscape'' which rejects all pages containing Netscape, even if they also contain other browsers.


I then show that alternative phrases appear frequently enough in dialog to warrant serious attention, yet present natural language search engines perform poorly on queries containing alternative phrases.

by approximating my semantic analysis into a form understood by a natural language search engine, I showed that the performance of that search engine improved dramatically.


The only other model that uses frontier lexicalization and that was tested on the standard WSJ split is Chiang (2000) who extracts a stochastic tree-insertion grammar or STIG (Schabes & Waters 1996) from the WSJ, obtaining 86.6% LP and 86.9% LR for sentences ≤ 40 words.

chiang's approach is limited in at least two respects.


They are based on dominance constraints (Marcus et al., 1983;Rambow et al., 1995) and extend them with parallelism (Erk and Niehren, 2000) and binding constraints.

lifting -reduction to an operation on underspecified descriptions is not trivial, and to our knowledge it is not known how this can be done.


The feature independence assumption of memory-based learning appears to be the harming cause: by its definition, IB1-IG does not give extra weight to apparently relevant interactions of feature values from different sources.

in nine out of the twelve rules that RIPPER produces, word graph features and system questions type features are explicitly integrated as joint left-hand side conditions.


Specifically, we do not make use of the possibilities offered by the interleaving of the RE and LC, as the examples we cover are too simple.

this setup enables RE, in principle, to make use of information about precisely how a previous reference to an entity has been realised.


At the start of turns, posture shift duration is approximately the same whether a new topic is introduced or not (2.5 seconds).

when ending a turn, speakers move significantly longer (7.0 seconds) when finishing a topic than when the topic is continued by the other interlocutor (2.7 seconds) (F(1,148)=17.9; p<0.001).


In our framework, however, every is the following (using qeq conditions, as in the LinGO ERG): So these composes via op spec to yield every dog: A slight complication is that the determiner is also syntactically selected by the N via the SPR slot (following Pollard and Sag (1994)).

from the standpoint of the compositional semantics, the determiner is the semantic head, and it is only its SPEC hole which is involved: the N must be treated as having an empty SPR hole.


While these transforms do reduce the size of the grammar, and modestly reduce the level of ambiguity from 1.96 to 1.92, they did not initially appear to improve recognition performance.

that was with the nuance parameter -node array optimization level set to the default value FULL.


Their precision values are not significantly 10 different from the baseline obtained by random selection.

to our expectation stated at the beginning of this section, the performance of ¢ ¡ and £ ¥ ¤ relative to the other AMs is not better for high-frequency data than for low-frequency data.


On the one hand, their method is expected to enhance existing encyclopedias, where vocabulary size is relatively limited, and therefore the quantity problems has been resolved.

encyclopedias extracted from the Web are not comparable with existing ones in terms of quality.


In hand-crafted encyclopedias, term descriptions are carefully organized based on domains and word senses, which are especially effective for human usage.

the output of Fujii's method is simply a set of unorganized term descriptions.


For this purpose, the use of large-scale corpora annotated with domains is desirable.

since those resources are prohibitively expensive, we used the "Nova" dictionary for Japanese/English machine translation systems 3 , which includes approximately one million entries related to 19 technical fields as listed below: aeronautics, biotechnology, business, chemistry, computers, construction, defense, ecology, electricity, energy, finance, law, mathematics, mechanics, medicine, metals, oceanography, plants, trade.


For this purpose, a number of quality rating methods for Web pages (Amento et al., 2000;Zhu and Gauch, 2000) can be used.

since Google (i.e., the search engine used in our system) rates the quality of pages based on hyperlink information, and selectively retrieves those with higher quality (Brin and Page, 1998), we tentatively regarded P Q (d) as a constant.


Second, P (d), which is a product of probabilities for N -grams in d, is quite sensitive to the length of d. In the cases of machine translation and speech recognition, this problem is less crucial because multiple candidates compared based on the language model are almost equivalent in terms of length.

since in our case length of descriptions are significantly different, shorter descriptions are more likely to be selected, regardless of the quality.


• Some non-verbal dependents, such as separable verbal prefixes (for example the an of anfangen 'begin'), predicative adjectives, and nouns governed by a copular verb or a support verb, can go into the right bracket (the prefix even forms one word with its following governor).

to verbs, these elements do not usually open up a new position for their dependents, which consequently have to be placed somewhere else.


It would also be interesting to attempt to describe other languages in this formalism, configurational languages such as English or French, as well as languages such as Russian where the surface order is mainly determined by the communicative structure.

german is an especially interesting case because surface order depends strongly on both the syntactic position (e.g.


Even choosing among the responses in (5) might be a pretty knowledge intensive business.

there are some clear strategies that might be pursued.


For example, Levin+ class 9.4 has three possible Word-Net senses for drop.

the WordNet sense 8 is not associated with any of the other classes; thus, it is considered to have a higher "information content" than the others.


This does not mean, however, that it must be wired into the computational model.

a computational model based on a small set of primitives that combine via simple composition rules will be more flexible in practice and easier to implement.


On the contrary, a computational model based on a small set of primitives that combine via simple composition rules will be more flexible in practice and easier to implement.

in the type-logical approach, the syntactic contents of a lexical entry is outlined by the following patern: <atom> : <syntactic category> the semantic contents obeys the following scheme: <λ-term> : <semantic type> This asymmetry may be broken by: 1. allowing λ-terms on the syntactic side (atomic expressions being, after all, particular cases of λ-terms), 2. using the same type theory for expressing both the syntactic categories and the semantic types.


Any ACG generates two languages, an abstract language and an object language.

the abstract language generated by G (A(G )) is defined as follows: In words, the abstract language generated by G is the set of closed linear λ-terms, built upon the abstract vocabulary Σ 1 , whose type is the distinguished type s. the object language generated by G (O(G )) is defined to be the image of the abstract language by the term homomorphism induced by the lexicon L : It may be useful of thinking of the abstract language as a set of abstract grammatical structures, and of the object language as the set of concrete forms generated from these abstract structures.


The object language of this second ACG is defined as follows: Then, a lexicon from Σ 1 to Σ 3 is defined: This allows the ACG G 13 to be defined as The abstract language shared by G 12 and G 13 contains the two following terms: The syntactic lexicon L 12 applied to each of these terms yields the same image.

it β-reduces to the following object term: the semantic lexicon L 13 yields the de re reading when applied to (2): and it yields the de dicto reading when applied to (3): Our handling of the two possible readings of (1) differs from the type-logical account of Morrill (1994) and Carpenter (1996).


In particular, they all satisfy the first requirement.

the satisfaction of the second requirement is, in most of the cases, an open problem.


Most of the current work on corpus annotation is concentrated on morphemics, lexical semantics and sentence structure.

it becomes more and more obvious that attention should and can be also paid to phenomena that reflect the links between a sentence and its context, i.e.


Loop 1 was generated more often than any other loop.

the small overall average number of feedback loops that have been carried out indicate that the fact they port little overhead to the Q&A system.


Features related to argument structure are not significantly correlated with VPE.

whether the two argument structures are identical is a factor approaching significance: in the two cases where they differ, no VPE happens (Q ).


For this reason we use the machine learning system Ripper (Cohen, 1996).

before we can use Ripper, we must discuss the issue of how our new trainable VPE module fits into the architecture of generation.


Finally, we extend this process so that the training procedure acts hierarchically on different portions of the messages at different times.

to the baseline flex system, the transducers that we induce are nondeterministic and stochastic -a given word sequence may align to multiple paths through the transducer.


In the above examples, the last words were common nouns.

the last word can also be a proper noun.


Thus, the two methods returned similar results.

we cannot expect good performance for other documents because CRL NE is limited to January, 1995.


Many statistical NLP tagging and parsing models are estimated by maximizing the (joint) likelihood of the fully-observed training data.

since these applications only require the conditional probability distributions, these distributions can in principle be learnt by maximizing the conditional likelihood of the training data.


Applications such as language modelling for speech recognition and EM procedures for estimating from hidden data either explicitly or implicitly require marginal distributions over the visible data (i.e., word strings), so it is not statistically sound to use MCLEs for such applications.

applications which involve predicting the value of the hidden variable from the visible variable (such as tagging or parsing) usually only involve the conditional distribution, which the MCLE estimates directly.


Thus one might expect the MLE to converge faster than the MCLE in situations where training data is not over-abundant, which is often the case in computational linguistics.

since the intended application requires a conditional distribution, it seems reasonable to directly estimate this conditional distribution from the training data as the MCLE does.


It seems to be difficult to find model classes for which the MLE and MCLE are both easy to compute.

often it is possible to find two closely related model classes, one of which has an easily computed MLE and the other which has an easily computed MCLE.


None of the models investigated here are stateof-the-art; the goal here is to compare two different estimation procedures, and for that reason this paper concentrated on simple, easily implemented models.

it would also be interesting to compare the performance of joint and conditional estimators on more sophisticated models.


Finally, a shallow−parser developed using these techniques will have to mirror the information contained in the training data.

for instance, if one trains such a tool on data were only non recursive NP chunks are marked 2 , then one will not be able to obtain richer information such as chunks of other categories, embeddings, syntactic functions... finite−state techniques rely on the development of a large set of rules (often based on regular expressions) to capture all the ways a constituent can expend.


Since the parser is running in a garbage-collected environment, it is hard to distinguish required memory from utilized memory.

unlike time and traversals which in practice can diverge, memory requirements match the number of edges in the chart almost exactly, since the large data structures are all proportional in size to the number of edges .


Given the highly unrestrictive nature of the treebank grammar, it is not very surprising that top-down filtering provides such little benefit.

this is a useful observation about real world parsing performance.


is slightly larger for the NOTRANS-FORM grammar, since the empty-reachable set is nonempty.

note that even for NOTRANSFORM, the largest SCC is smaller than the empty-reachable set, since empties provide direct entry into some of the lower SCCs, in particular because of WH-gaps.


To reach a new category always requires the use of at least one overt word.

for spans of size 6 or so, enough words exist that the same high saturation effect will still be observed.


11 Without unaries, the more gradual saturation growth increases the total exponent, more so for NOUNARIESLOW than NOUNARIESHIGH.

note that for spans around 8 and onward, the saturation curves are essentially constant for all settings.


Given the large SCCs seen in section 4.1, phrasal categories, to a first approximation, might as well be wildcards, able to match any span, especially if empties are present.

the tags are, in comparison, very restricted.


In the case of about the tags 'in' (for: preposition) or 'rb' (for: adverb) would be appropriate.

since the POS tagger cannot resolve this ambiguity from local context, the underspecified tag 'about' is assigned, instead.


However, since the POS tagger cannot resolve this ambiguity from local context, the underspecified tag 'about' is assigned, instead.

this can in turn lead to misclassification in the chunker.


Postprocessing mainly consists of shortening the tree from the instance base so that it covers only those parts of the chunk that could be matched.

if the match is done on the lexical level, a correction of tagging errors is possible if there is enough evidence in the instance base.


In the current version of the algorithm, generalization heavily relies on lexical and part-of-speech information.

a richer set of backing-off strategies that rely on larger domains of structure are easy to envisage and are likely to significantly improve recall performance.


The correlation was significant both for frequencies recreated by smoothing over adjectives (r = .214, p < .05) and over nouns (r = .232, p < .05).

co-occurrence frequency recreated using the Jensen-Shannon divergence was not reliably correlated with plausibility.


If W ij > 0, it means that if document x is related to T j , it may also have some contribution ( W ij ) to topic T j .

if W ij < 0, it means the two topics are negatively correlated, and a document x will not be related to both T j and T i .


The Probability-based Translation MEMory (PTMEM) was created by associating with each French phrase the English equivalent that corresponded to the alignment of highest probability.

to other TMEMs, our TMEMs explicitly encode not only the mutual translation pairs but also their corresponding word-level alignments, which are derived according to a certain translation model (in our case, IBM model 4).


Because of this, a statistical-based MT system will have trouble producing a translation that uses the phrase "kick the bucket", no matter what decoding technique it employs.

if the two phrases are stored in the TMEM, producing such a translation becomes feasible.


Tagging all sentences for the correct structures, however, is an intractable task for a human coder.

while it is feasible to have this information collected computationally through our parser, we are still faced with the problem of competing parses for many sentences.


The probability of a rule is inferred by an iterative training procedure with an extended version of the inside-outside algorithm.

only those analyses are considered that meet the tagged brackets (here syllable brackets).


The next group of systems were the two rulebased systems, ICF and RBS, which were not statistically different from one another.

sPOT was statistically better than both of these systems (p .01).


This is also somewhat to be expected, since the baseline systems were intended to be the simplest systems constructable.

it would have been a possible outcome for SPOT to not be different than either system, e.g.


There is increasing interest in techniques for evaluating Natural Language Generation (NLG) systems.

we are not aware of any previously reported evaluations of NLG systems which have rigorously compared the task effectiveness of an NLG system to a non-NLG alternative.


Task evaluations (for example, (Young, 1999)) are typically done by showing human subjects different texts, and measuring differences in an outcome variable, such as success in performing a task.

despite the above work, we are not aware of any previous evaluation which has compared the effectiveness of NLG texts at meeting a communicative goal against the effectiveness of non-NLG control texts.


(Soccer results are much more frequent.)

we now regret this methodological flaw and would like to clean up a little more (as done in the patches), or add back soccer results.


The appositive phrases usually provide descriptions of attributes of a person.

the preprocessing component described in Section 2.1 does produce errors in appositive extraction, which are filtered out by syntactic and semantic tests.


Given a unique tree structure for a sentence, governor markup may be read off the tree.

in view of the fact that robust broad coverage parsers frequently deliver thousands, millions, or thousands of millions of analyses for sentences of free text, basing annotation on a unique tree (such as the most probable tree analysis generated by a probabilistic grammar) appears arbitrary.


All systems supported travel planning and utilized some form of mixedinitiative interaction.

the systems varied in several critical dimensions: (1) They targeted different back-end databases for travel information; (2) System modules such as ASR, NLU, TTS and dialogue management were typically different across systems.


We removed system turn duration from the model, to determine whether FlightInfoC would become a significant predictor.

the model fit decreased and FlightInfoC was not a significant predictor.


In word N-grams, accurate word prediction can be expected, since a word dependent, unique connectivity from word to word can be represented.

the number of estimated parameters, i.e., the number of combinations of word transitions, is Î AE in vocabulary Î .


In class N-grams with classes, the number of estimated parameters is decreased from Î AE to AE .

accuracy of the word prediction capability will be lower than that of word Ngrams with a sufficient size of training data, since the representation capability of the word dependent, unique connectivity attribute will be lost for the approximation base word class.


One may partially remedy this problem by decreasing the learning rate parameter Y during the updates.

this is rather ad hoc since it is unclear what is the best way to do so.


The resulting formulation is closely related to (2).

instead of looking at one example at a time as in an online formulation, we incorporate all examples at the same time.


Some other aspects of the system design (such as dynamic programming, features used, etc) have more impact on the performance.

due to the limitation of space, we will not discuss their impact in detail.


This additional level of detail depends on the request itself and on the available information.

here we consider the former factor, viewing it as an initial filter that will guide the content planning process of an enhanced QA system.


e.g., "name", "type of", "called" Comparison clues e.g., "similar", "differ", "relate" Intersection clues superlative ADJ, ordinal ADJ, relative clause Topic Itself clues e.g., "show", "picture", "map"  answer (which is similar to our Focus).

our Focus decision tree includes additional attributes in its second split (these attributes are added by dprog because they improve predictive performance on the training data).


Further, the predictive performance obtained for the set Good4617 is only slightly better than that obtained for the set All5145.

since the set of good queries is 10% smaller, it is considered a better option.


In some applications, such as language modeling, this is not the case; instead, the f i are computed on the fly.

with a bit of thought, those data structures also can be rearranged.


In particular, let f # (x, y) = i f i (x, y) Then, solve numerically for δ i in the equation Notice that in the special case where f # (x, y) is a constant f # , Equation 4 reduces to Equation 2.

for training instances where f # (x j , y) < f # , the IIS update can take a proportionately larger step.


So far, we have only considered TAG grammars in which each elementary tree is assigned a semantics that contains precisely one atom.

there are cases where an elementary tree either has an empty semantics, or a semantics that contains multiple atoms.


Gardent and Thater (2001) also propose a con-straint based approach to generation working with a variant of TAG.

the performance of their system decreases rapidly as the input gets larger even when when working with a toy grammar.


Possible linearizations of the XTAG representation are generated and then evaluated by a language model to pick the best possible linearization, as in Nitrogen.

the sentence realization system code-named Amalgam (A Machine Learned Generation Module) Gamon et al., 2002b) employs a series of linguistic operations which map a semantic representation to a surface syntactic tree via intermediate syntactic representations.


These may include named entity taggers, WordNet, parsers, hand-tagged corpora, and ontology lists (Srihari and Li,00;Harabagiu et al.,01;Hovy et al.,01;Prager et al.,01).

at the recent TREC-10 QA evaluation (Voorhees,01), the winning system used just one resource: a fairly extensive list of surface patterns (Soubbotin and Soubbotin,01).


We have to show that all other candidates would also be excluded (for lack of the relevant rules in the non-recursive part).

if it were the recursion in by construction step (20c,d) (only violation-free recursion is possible).


That much was previously shown by Markus Hiller and Paul Smolensky (Frank and Satta, 1998), using similar examples.

at least some OT grammars ought to describe regular relations.


For task 2 we adopt the substitutional approach advocated by Crouch (1995): The semantic representation of the target § is a copy of the source § where target parallel elements have been substituted for source parallel elements ( ) .

to Higher-Order Unification (HOU) (Dalrymple et al., 1991) substitution is deterministic: Ambiguities somehow cropping up in the interpretation process (i.e.


Finally, Hugh ½¿ is repeated in the same sentence.

there are significant practical obstacles to comparing the performance of different pronominalization algorithms using corpus matching criteria instead of "quality" as evaluated by human judges.


In the D&R algorithm, the problem stems from the fact that DD are generated recursively: if when generating a DD for some entity The solution adopted by (Dale and Haddock, 1991) is to stipulate that facts from the knowledge base can only be used once within a given call to the algorithm.

the solution follows, in the present algorithm (as in SPUD), from its integration with surface realisation.


By the F 1 measure used in the experiments in section 4, an induced dependency PCFG scores 48.2, compared to a score of 82.1 for a supervised PCFG read from local trees of the treebank.

a supervised dependency PCFG scores only 53.5, not much better than the unsupervised version, and worse than a right-branching baseline (of 60.0).


PP accuracy is lower, which is not surprising considering that PPs tend to appear rather optionally and in contexts in which other, easier categories also frequently appear.

embedded sentence recall is substantially higher, possibly because of more effective use of the top-level sentences which occur in the signature context -.


It is certainly worth exploring methods which supplement or replace tagged input with lexical input.

we address here the more serious criticism: that our results stem from clues latent in the treebank tagging information which are conceptually posterior to knowledge of structure.


It goes to some lengths to handle complex cases such as adjunction and where two or more empty nodes' paths cross (in these cases the pattern extracted consists of the union of the local trees that constitute the patterns for each of the empty nodes).

given the low frequency of these constructions, there is probably only one case where this extra complexity is justified: viz., the empty compound SBAR subtree shown in Figure 3.


This model deals with phonetic errors significantly better than previous models since it allows a much larger context size.

this model makes residual errors, many of which have to do with word pronunciation.


The bigram probabilities are estimated from the training data by maximum likelihood estimation (MLE).

the intrinsic problem of MLE is that of data sparseness: MLE leads to zero-value probabilities for unseen bigrams.


We notice that LF probability of Equation 3is very similar to that proposed by Seymore and Rosenfeld (1996), where the loss function is From Equations (2) and (3), we can see that lower LF probability is strongly correlated with lower perplexity.

we found that LF probability is suboptimal as a pruning criterion, evaluated on CER in our experiments.


Classical cluster models are symmetric in that the same clusters are employed for both predicted and conditional words.

the symmetric cluster model is suboptimal in practice.


If we put the word "Tuesday" into the cluster WEEKDAY, we decompose the probability When each word belongs to one class, simple math shows that this decomposition is a strict equality.

when smoothing is taken into consideration, using the clustered probability will be more accurate than using the non-clustered probability.


It is an interesting question whether our techniques for hard clustering can be extended to soft clustering.

soft clustering models tend to be larger than hard clustering models because a given word can belong to multiple clusters, and thus a training instance P(w i |w i-2 w i-1 ) can lead to multiple counts instead of just 1.


When the SLM is interpolated with the 3-gram, the h-2+opposite+parent scheme performed the best, reducing the PPL of the baseline SLM by 3.3%.

the parent and opposite+parent schemes are both worse than the baseline, especially before the EM training and with ¢ =0.0.


The Jaccard measure is the cardinality ratio of the intersection and union of attribute sets (atts(w n ) is the attribute set for w n ): The generalised Jaccard measure allows each relation to have a significance weight (based on word, attribute and relation frequencies) associated with it: Grefenstette originally used the weighting function: where f (w i , a j ) is the frequency of the relation and n(a j ) is the number of different words a j appears in relations with.

context Description W(L 1 R 1 ) one word to left or right W(L 1 ) one word to the left W(L 1,2 ) one or two words to the left W(L 1−3 ) one to three words to the left Table 1: Window extractors we have found that using the t-test between the joint and independent distributions of a word and its attribute: gives superior performance (curran and Moens, 2002) and is therefore used for our experiments.


the point where MINIPAR can process 75M words, we get a best direct match score of 23.5.

we can get the same resultant accuracy by using SEXTANT on a corpus of 116M words or W(L 1 R 1 ) on a corpus of 240M words.


In the future, when we see an NC mapped to this rule, we will assign this semantic relationship to it.

the following NCs, having the CP A01 (Body Regions) and M01 (Persons), do not have the same relationship between the component words: abdomen patients, arm amputees, chest physicians, eye patients, skin donor.


(2001) mention that dynamic programming can be used to compute the statistics required for conditional estimation of log-linear models based on context-free grammars where the properties can include arbitrary functions of the input string.

miyao and Tsujii (2002) (which 1 because we use conditional estimation, also known as discriminative training, we require at least some discriminating information about the correct parse of a string in order to estimate a stochastic unification grammar.


The algorithms described here are exact, but because we are working with unification grammars and apparently arbitrary graphical models we cannot polynomially bound their computational complexity.

it seems reasonable to expect that if the linguistic dependencies in a sentence typically factorize into largely non-interacting cliques then the dynamic programming methods may offer dramatic computational savings compared to current methods that enumerate all possible parses.


We use the symbol P r(•) to denote general probability distributions with (nearly) no specific assumptions.

for model-based probability distributions, we use the generic symbol p(•).


It even detected frequent sentenceto-sentence translations, since we only imposed a relative length limit for phrasal translations (Section 3).

some of them, such as the one with (in cantonese), are wrong.


Using only trigrams obtains the best result for the BLEU score.

the BLEU metric may not be affected by the syntactic aspect of translation quality, and as we saw in Figure 4, we can improve the syntactic quality by introducing the PCFG using some corpus selection techniques.


But first observe that Candidate 1 shares "It is a guide to action" with Reference 1, "which" with Reference 2, "ensures that the military" with Reference 1, "always" with References 2 and 3, "commands" with Reference 1, and finally "of the party" with Reference 2 (all ignoring capitalization).

candidate 2 exhibits far fewer matches, and their extent is less.


the dependencies are typically derived from a context-free phrase structure tree using simple head percolation heuristics.


According to an evaluation of unlabeled word-word dependencies, our best model achieves a performance of 89.9%, comparable to the figures given by Collins (1999) for a linguistically less expressive grammar.

to Gildea (2001), we find a significant improvement from modeling wordword dependencies.


However, slightly worse performance is obtained for LexCatDep, a model which is identical to the original LexCat model, except that c S is also conditioned on c H , the lexical category of the head node, which introduces a dependency between the lexical categories.

since there is so much information in the lexical categories, one might expect that this would reduce the effect of conditioning the expansion of a constituent on its head word w. we did find a substantial effect.


If applied to a text with ¦ noun phrases, this algorithm produces a total of noun phrase pairs.

a number of filters can reasonably be applied at this point.


For our experiments we implemented the standard Co-Training algorithm (as described in Section 3) in Java using the Weka machine learning library 3 .

to other Co-Training approaches, we did not use Naive Bayes as base classifiers, but J48 decision trees, which are a Weka re-implementation of C4.5.


The resulting Co-Training curve degrades substantially.

with a training size of 1000 and 2000 the Co-Training curves are above their baselines.


With L=500 the Co-Training curve is way below the baseline.

with L=1000 and L=2000 Co-Training does show some improvement.


Supervised learning of reference resolution classifiers is expensive since it needs unknown amounts of annotated data for training.

reference resolution algorithms based on these classifiers achieve reasonable performance of about 60 to 63% F-measure (Soon et al., 2001).


In this case, therefore, Co-Training seems to be able to save manual annotation work.

the definition of the feature views is non-trivial for the task of training a reference resolution classifier, where no obvious or natural feature split suggests itself.


Even when a system is deployed, it often keeps evolving, either because customers want to do different things with it, or because new tasks arise out of developments in the underlying application.

real customers of a deployed system may not be willing to give detailed feedback.


This is reflected in the User Satisfaction scores in the tree.

dialogues which are long (TurnsOnTask 79 ) can be satisfactory (User Satisfaction = 15.2) as long as the task that is completed is long, i.e., if ground arrangements are made in that dialogue (Task Completion=2).


Transliteration between languages that use similar alphabets and sound systems is very simple.

transliterating names from Arabic into English is a non-trivial task, mainly due to the differences in their sound and writing systems.


According to this model, the transliteration probability is given by the following equation: The transliterations proposed by this model are generally accurate.

one serious limitation of this method is that only English words with known pronunciations can be produced.


To use these normalized counts to score and rank the first name/last name combinations in a way similar to a unigram language model, we would get the following name/score pairs: (John Keele, 0.003), (John Kyl, 0.001), (Jon Keele, 0.0002), and ).

the normalized phrase counts for the possible full names are: (Jon Kyl, 0.8976), (John Kyl, 0.0936), (John Keele, 0.0087), and (Jon Keele, 0.0001), which is more desirable as Jon Kyl is an often-mentioned US Senator.


In some cases, this criterion is too rigid, as it will consider perfectly acceptable translations as incorrect.

since we use it mainly to compare our results with those obtained from the human translations and the commercial system, this criterion is sufficient.


Although they do not consider the task of classifying reviews, it seems their algorithm could be plugged into the classification algorithm presented in Section 2, where it would replace PMI-IR and equation 3in the second step.

pMI-IR is conceptually simpler, easier to implement, and it can handle phrases and adverbs, in addition to isolated adjectives.


This suggests the hypothesis that a good movie will often contain unpleasant scenes (e.g., violence, death, mayhem), and a recommended movie re-view may thus have its average semantic orientation reduced if it contains descriptions of these unpleasant scenes.

if we add a constant value to the average SO of the movie reviews, to compensate for this bias, the accuracy does not improve.


This is likely also the explanation for the lower accuracy of the Cancun reviews: good beaches do not necessarily add up to a good vacation.

good automotive parts usually do add up to a good automobile and good banking services add up to a good bank.


Inspection of Equation (3) shows that it takes four queries to calculate the semantic orientation of a phrase.

i cached all query results, and since there is no need to recalculate hits("poor") and hits("excellent") for every phrase, each phrase requires an average of slightly less than two queries.


A useful feature of such patterns is that when we search for them on the Web they usually produce many hits, thus making statistical approaches applicable.

searching for strict validation statements generally results in a small number of documents (if any) and makes statistical methods irrelevant.


We use a Web-mining algorithm that considers the number of pages retrieved by the search engine.

qualitative approaches to Web mining (e.g.


In a second type of experiment, for every question and its corresponding answers the program chooses the answer with the highest validity score and calculates a relative threshold on that basis ).

the relative threshold should be larger than a certain minimum value.


Another conclusion is that the relative threshold demonstrates superiority over the absolute threshold in both test sets (average 2.3%).

if the percent of the right answers in the answer set is lower, then the efficiency of this approach may decrease.


This brief overview shows that the main reason for the use of POS tags in parsing is that they provide useful generalizations and (thereby) counteract the sparse data problem.

there are two objections to this reasoning.


From the sequence of predicted function-chunk codes, the complete chunking and function assignment can be reconstructed.

predictions can be inconsistent, blocking a straightforward reconstruction of the complete shallow parse.


(1999) achieve 71.2 F-score for grammatical relation assignment on automatically tagged and chunked text after training on about 40,000 Wall Street Journal sentences.

to these studies, we do not chunk before finding grammatical relations; rather, chunking is performed simultaneously with headword function tagging.


Owing to the fact that deep and shallow technologies are complementary in nature, integration is a nontrivial task: while SNLP shows its strength in the areas of efficiency and robustness, these aspects are problematic for DNLP systems.

dNLP can deliver highly precise and fine-grained linguistic analyses.


Two workshops on Automatic Summarization were held in 2000 and 2001.

the area is still being fleshed out: most past efforts have focused only o n single-document summarization (Mani 2000), and no standard test sets and large scale evaluations have been reported or made available to the Englishspeaking research community except the TIPSTER SUMMAC Text Summarization evaluation (Mani et al.


Most of the techniques adopted by NeATS are not new.

applying them in the proper places to summarize multiple documents and evaluating the results on large scale common tasks are new.


At K ≥ 1, out of 1424 instances, i.e., sentences, 707 of them are marked positive and 717 are marked negative, so positive and negative instances are evenly spread across the data.

at K ≥ 5, there are only 72 positive instances.


rule-based approaches lack the ability of coping with the problems of robustness and portability.


Collins and Singer (1999) investigated named entity classification using Adaboost, CoBoost, and the EM algorithm.

features were extracted using a parser, and performance was evaluated differently (the classes were person, organization, location, and noise).


Due to the above reason, it is unreasonable to expect the performance of the upper case NER to match that of the mixed case NER.

we still manage to achieve a considerable reduction of errors between the two NERs when they are tested on the official MUC-6 and MUC-7 test data.


The algorithm is shown in Figure  2 in a pseudo-code.

this method has the problem of being computationally costly in training, because the negative examples are created for all the classes other than the true class, and the total number of the training examples becomes large (which is equal to the number of original training examples multiplied by the number of classes).


Brill studied transformation-based error-driven learning (TBL) (Brill, 1995), which conducts POS tagging by applying the transformation rules to the POS tags of a given sentence, and has a resemblance to revision learning in that the second model revises the output of the first model.

our method differs from TBL in two ways.


On the other hand, the computational cost becomes large because each learner is trained using every training data and answers for every test data.

multi-stage methods can decrease the computational cost, and seem to be effective when a large amount of data is used or when a learner with high computational cost such as SVMs is used.


A particular strength is their ability to model both general rules and specific exceptions in a single framework (van den Bosch and Daelemans, 1999).

they have generally only been used in supervised learning techniques where a class label or tag has been associated to each feature vector.


When it is completely predictable, as in SLOVENE the performance approaches 100%; similarly a large majority of the less frequent words in English are completely regular, and accordingly the performance on EPT is very good.

in other cases, where the morphology is very irregular the performance will be poor.


This approach makes the task comparable to classic word sense disambiguation (WSD), which is also concerned with distinguishing between possible word senses/interpretations.

whereas a classic (supervised) WSD algorithm is trained on a set of labelled instances of one particular word and assigns word senses to new test instances of the same word, (supervised) metonymy recognition can be trained on a set of labelled instances of different words of one semantic class and assign literal readings and metonymic patterns to new test instances of possibly different words of the same semantic class.


Inferring from the metonymy in (4) that "Germany" in "Germany lost a fifth of its territory" is also metonymic, e.g., is wrong and lowers precision.

wrong assignments (based on headmodifier relations) do not constitute a major problem as accuracy is very high (90.2%).


We showed that syntactic head-modifier relations are a high precision feature for metonymy recognition.

basing inferences only on the lexical heads seen in the training data leads to data sparseness due to the large number of different lexical heads encountered in natural language texts.


We define , in article alignment J and E. When we compare the validity of two sentence alignments in the same article alignment, the rank order of sentence alignments obtained by applying SntScore is the same as that of SIM because they share a common AVSIM.

when we compare the validity of two sentence alignments in different article alignments, SntScore prefers the sentence alignment with the more similar (high AVSIM) article alignment even if their SIM has the same value, while SIM cannot discriminate between the validity of two sentence alignments if their SIM has the same value.


For example, with our simple tree A B X Y Z if nodes A and B are considered as one elementary tree, with probability P elem (t a |A ⇒ BZ), their collective children will be reordered with probability giving the desired word ordering XZY.

computational complexity as well as data sparsity prevent us from considering arbitrarily large elementary trees, and the number of nodes considered at once still limits the possible alignments.


In fact, Melamed defines a probability distribution P (links(u, v)|cooc(u, v), λ + , λ − ) which appears to make our work redundant.

this distribution refers to the probability that two word types u and v are linked links(u, v) times in the entire corpus.


Treebank-based probabilistic parsing has been the subject of intensive research over the past few years, resulting in parsing models that achieve both broad coverage and high parsing accuracy (e.g., Collins 1997;Charniak 2000).

most of the existing models have been developed for English and trained on the Penn Treebank (Marcus et al., 1993), which raises the question whether these models generalize to other languages, and to annotation schemes that differ from the Penn Treebank markup.


This is not surprising, as the perfect tags (but not the TnT tags) include grammatical function labels.

we also observe a dramatic reduction in coverage (to about 65%).


We added grammatical functions to both the baseline model and the C&R model, as we predicted that this would allow the model to better capture the wordorder facts of German.

this prediction was not borne out: performance with grammatical functions (on TnT tags) was slightly worse than without, and coverage dropped substantially.


On the other hand, a performance increase occurs if the tagger also provides grammatical function labels (simulated in the perfect tags condition).

this comes at the price of an unacceptable reduction in coverage.


As a possible explanation we considered lack of training data: Negra is about half the size of the Penn Treebank.

the learning curves for the three models failed to produce any evidence that they suffer from sparse data.


An obvious challenge for our approach is thus to identify suitable shallow knowledge sources that can deliver compatible constraints for HPSG parsing.

chunks delivered by state-of-the-art shallow parsers are not isomorphic to deep syntactic analyses that explicitly encode phrasal embedding structures.


In addition, some studies investigate the extraction of synonymous words and/or patterns from bilingual corpora (Barzilay and Mckeown, 2001;Shimohata and Sumita, 2002).

these methods can only extract synonymous expressions which occur in the bilingual corpus.


This is mainly because Method 3 can only extract synonymous collocations which occur in the bilingual corpus.

our method uses the bilingual corpus to train the translation probabilities, where the translations are not necessary to occur in the bilingual corpus.


The lack of syntactic information makes the building of semantic space models relatively straightforward and language independent (all that is needed is a corpus of written or spoken text).

this entails that contextual information contributes indiscriminately to a word's meaning.


The η 2 statistic revealed that only 5.2% of the variance was accounted for.

a reliable effect of distance was observed for all dependency-based models (p < .001).


Taxonomy related relations (e.g., synonymy, antonymy, hyponymy) can be reliably distinguished from conceptual and phrasal association.

no reliable differences were found between closely associated relations such as antonymy and synonymy.


This points to the fact that rich context encodings with a wide range of dependency relations are promising for capturing lexical semantic distinctions.

the performance for maximum context specification was lower, which indicates that collapsing all dependency relations is not the optimal method, at least for the tasks attempted here.


There have been rigourous studies of Boolean operators for information retrieval, including the pnorms of  and the matrix forms of Turtle and Croft (1989), which have focussed particularly on mathematical expressions for conjunction and disjunction.

typical forms of negation (such as NOT p = 1−p) have not taken into account the relationship between the negated argument and the rest of the query.


If arbitrary wordreorderings are permitted, the search problem is NP-hard.

if we restrict the possible word-reorderings in an appropriate way, we obtain a polynomial-time search algorithm.


Because of the difficulties a pronoun resolution algorithm encounters in spoken dialogue, previous approaches were applied only to tiny domains, they needed deep semantic analysis and discourse processing and relied on hand-crafted knowledge bases.

we build on our existing anaphora resolution system and incrementally add new features specifically devised for spoken dialogue.


Spoken dialogue contains more pronouns with non-NP-antecedents than written text does.

pronouns with NP-antecedents (like 3rd pers.


Moreover, a history of just two previous embeddings (as we get it with ) is too limited in a heavily recursive setting like word formation: recursive embeddings of depth four occur in realistic text.

we can exploit more effectively the "mildly context-free" characteristics of morpholog-ical grammars (at least of German) discussed in sec.


As the size results show, the non-deterministic FSAs constructed by the selective method are more complex (and hence resource-intensive in minimization) than the ones produced by the "plain" parameterized version.

the difference in exactness of the approximizations has to be taken into account.


These machine learning approaches have been successful for these tasks, achieving accuracy comparable to the knowledge-engineering approach.

for the full-scale ST task of generic IE from free texts, the best reported method to date is still the knowledge-engineering approach.


The maximum entropy (ME) framework is a recent learning approach which has been successfully used in various NLP tasks such as sentence segmentation, part-of-speech tagging, and parsing (Ratnaparkhi, 1998).

to our knowledge, ours is the first research effort to have applied ME learning to the full-scale ST task.


In this case, only the first occurrence of OQUELI COLINDRES should be used as a positive example for the human target slot.

aLICE does not have access to such information, since the MUC-4 training documents are not annotated (i.e., only templates are provided, but the text strings in a document are not marked).


Learning approaches have been shown to perform on par or even outperform knowledge-engineering approaches in many NLP tasks.

the full-scale scenario template IE task was still dominated by knowledge-engineering approaches.


For example, in Figure 1(c), (triggered ( C-DATE -ADV)) is needed to extract the date entity.

the same pattern is likely to be applied to texts in other domains as well, such as "The Mexican peso was devalued and triggered a national financial crisis last week."


A story about a plane crash and another story about the funeral of the crash victims are considered to be linked.

a story about hurricane Andrew and a story about hurricane Agnes are not linked because they are two different events.


In the LNK task, incorrectly flagging two stories as being on the same event is considered a false alarm.

in the NED task, incorrectly flagging two stories as being on the same event will cause the true first story to be missed.


Most conventional systems uniquely determine the result of the discourse understanding, i.e., the dialogue state, after each user utterance.

multiple dialogue states are created from the current dialogue state and the speech understanding results corresponding to the user utterance, which leads to ambiguity.


Confusion matrix has been employed effectively in spoken document retrieval (Singhal et al, 1999 andSrinivasan et al 2000) and to minimize speech recognition errors (Shen et al, 1998).

when such method is used directly to correct speech recognition errors, it tends to bring in too many irrelevant terms (Ng 2000).


Many spoken document retrieval (SDR) systems took advantage of this fact in reducing the speech recognition and matching errors .

to SDR, very little work has been done on Chinese spoken query processing (SQP), which is the use of spoken queries to retrieval textual documents.


Suppose the user issues a speech query: " ¢ © ¤ ¥ £ ¦ §¨ " (please help me to collect some information about Bin Laden).

the result of speech recognition with errors is: " (please) (help) Note that there are 4 mis-recognized characters which are underlined.


Moreover, they assumed that the input is text only, which does not contain errors.

spoken utterances include various information such as the interval between utterances, the presence of barge-in and so on, which can be utilized to judge the user's character.


If a user is identified as having the high skill level, the dialogue management is carried out in a user-initiated manner; namely, the system generates only open-ended prompts.

when user's skill level is detected as low, the system takes an initiative and prompts necessary items in order.


Then the original discourse marker may not be appropraite in the revised sentence plan.

for example, consider how the application of the following revision types requires different lexicalizations for the initial discourse markers: Clause Aggregation: The merging of two main clauses into one main clause and one subordinate clause: Clause Demotion: Two main clauses are merged where one of them no longer has a clause structure: The happy man went home.¨the man was poor.


This is because there is a five-year statute of limitations on that crime.

there is no statute of limitations in murder cases."


In other words, almost one of every four clause revisions potentially forces a change in discourse marker lexicalizations and one in every two discourse markers occur near a clause revision boundary.

the "penalty" associated with incorrectly selecting discourse markers is fairly high leading to confusing sentences, although there is no cognitive science evidence that states exactly how high for a typical reader, despite recent work in this direction (Tree and Schrock, 1999).


Morphemes are the smallest meaning-bearing elements of language and could be used as lexical units instead of entire words.

the construction of a comprehensive morphological lexicon or analyzer based on linguistic theory requires a considerable amount of work by experts.


While the former is a rather intuitive measure, the latter may not appear as intuitive.

the proportion of hapax legomena may be interpreted as a measure of the richness of the text.


This is reminiscent of Church's statement that '[t]he first mention of a word obviously depends on frequency, but surprisingly, the second does not.'

(Church, 2000) Church was concerned with language modeling, and in particular cache-based models that overcome some of the limitations introduced by a Markov assumption.


Comparing different probability models in terms of their effects on classification under a Naive Bayes assumption is likely to yield very conservative results, since the Naive Bayes classifier can perform accurate classifications under many kinds of adverse conditions and even when highly inaccurate probability estimates are used (Domingos and Pazzani, 1996;Garg and Roth, 2001).

an evaluation in terms of document classification has the advantages, compared with language modeling, of computational simplicity and the ability to benefit from information about non-occurrences of words.


For example, the natural overdispersed variant of the multinomial model is the Dirichlet-multinomial mixture, which adds just a single parameter that globally controls the overall variation of the entire vocabulary.

church, Gale and other have demonstrated repeatedly (church and Gale, 1995;church, 2000) that adaptation or "burstiness" are clearly properties of individual words (word types).


al, 1996) is another well-known method that can perform homogeneous context extension.

figure 2 illustrates heterogeneous context extension, in other words, this type of extension involves taking more information about other types of contextual features.


This insertion/deletion scheme contributed to the simplicity of this representation of the translation processes, allowing a sophisticated application to run on an enormous bilingual sentence collection.

it is apparent that the weak modeling of those phenomena will lead to inferior performance for language pairs such as Japanese and English.


All of these methods bias the training and/or decoding with phrase-level examples obtained by preprocessing a corpus (Och et al., 1999;Watanabe et al., 2002) or by allowing a lexicon model to hold phrases (Marcu and Wong, 2002).

the chunk-based translation model holds the knowledge of how to construct a sequence of chunks from a sequence of words.


We observed that after iteration 103 only 10% of the patterns are "good", the rest are secondary.

in the first 103 iterations, over 90% of the patterns are good Management Succession patterns.


(Thelen and Riloff, 2002) presents a very similar technique, in the same application as the one described in (Yangarber et al., 2002).

7 (Thelen and Riloff, 2002) did not focus on the issue of convergence, and on leveraging negative categories to achieve or improve convergence.


This is a very aggressive strategy, and it is likely to adversely affect parsing accuracy.

more lenient strategies were found to require too much space for the chart to be held in memory.


In all cases, the CCG derivation includes all longrange dependencies.

with the models that exclude certain kinds of dependencies, it is possible that a word is conditioned on no dependencies.


For instance, the dependency between the head of a noun phrase and the head of a reduced relative clause (the shares bought by John) is captured by the SD model, since shares and bought are both heads of the local trees that are combined to form the complex noun phrase.

in the SD model the probability of this dependency can only be estimated from occurrences of the same construction, since dependency relations are defined in terms of local trees and not in terms of the underlying predicate-argument struc-  ture.


Based on the relevance score, we can produce a full ranking of all the summaries in the corpus.

to (Brandow et al., 1995) who run 12 Boolean queries on a corpus of 21,000 documents and compare three types of documents (full documents, lead extracts, and ANES extracts), we measure retrieval performance under more than 300 conditions (by language, summary length, retrieval policy for 8 summarizers or baselines).


Parent annotation lets us indicate an important feature of the external environment of a node which influences the internal expansion of that node.

lexicalization is a (radical) method of marking a distinctive aspect of the otherwise hidden internal contents of a node which influence the external distribution.


Clearly, information about long-distance relationships is vital for semantic interpretation.

such constructions prove to be difficult for stochastic parsers (Collins et al., 1999) and they either avoid tackling the problem (Charniak, 2000;Bod, 2003) or only deal with a subset of the problematic cases (Collins, 1997).


Theoretically, the 'best' way to combine the trace tagger and the parsing algorithm would be to build a unified probabilistic model.

the nature of the models are quite different: the finite-state model is conditional, taking the words as given.


Conditional parsing algorithms do exist, but they are difficult to train using large corpora (Johnson, 2001).

we show that it is quite effective if the parser simply treats the output of the tagger as a certainty.


The idea of threading EEs to their antecedents in a stochastic parser was proposed by Collins (1997), following the GPSG tradition (Gazdar et al., 1985).

we extend it to capture all types of EEs.


Assuming that five rules on average are applied to translate a sentence, the number of sentence translations becomes 5 × C + C = 60, 000 for testing all rules.

to add a rule, the entire corpus must be re-translated because it is unknown which MT results will change by adding a rule.


This table shows that the test corpus BLEU score and the subjective Focusing on the subjective quality of the proposed methods, some MT results were degraded from the baseline due to the removal of rules.

the subjective quality levels were relatively improved because our methods aim to increase the portion of correct MT results.


(ii) While we can obtain large parallel corpora in the long run, to have them manually wordaligned would be too time-consuming and would defeat the original purpose of getting a sensetagged corpus without manual annotation.

are current word alignment algorithms accurate enough for our purpose?


Domain Dependence The accuracy figure of M1 for each noun is obtained by training a WSD classifier on the manually sense-tagged training data (with lumped senses) provided by SENSEVAL-2 organizers, and testing on the corresponding official test data (also with lumped senses), both of which come from similar domains.

the P1 score of each noun is obtained by training the WSD classifier on a mixture of six parallel corpora, and tested on the official SENSEVAL-2 test set, and hence the training and test data come from dissimilar domains in this case.


Therefore, unknown words were apt to be either concatenated as one word or divided into both a combination of known words and a single word that consisted of more than one character.

this model has the potential to correctly detect any length of unknown words.


In general, the higher the OOV is, the more difficult detecting word segments and their POS categories is.

the difference between accuracies for short and long words was about 1% in recall and 2% in precision, which is not significant when we consider that the difference between OOVs for short and long words was 4%.


Since the position of a word plays an important role as a syntactic constraint in English, the methods are successful even with local information.

these methods are not appropriate for chunking Korean and Japanese, because such languages have a characteristic of partially free wordorder.


Firstly supertags encode much more syntactical information than POS tags, which makes supertagging a useful pre-parsing tool, so-called, almost parsing (Srinivas and Joshi, 1999).

as the term 'supertagging' suggests, the time complexity of supertagging is similar to that of POS tagging, which is linear in the length of the input sentence.


As far as supertagging is concerned, word context forms a very large space.

for each word in a given sentence, only a small part of features in the space are related to the decision on supertag.


Our algorithm, which is coded in Java, takes about 10 minutes to supertag the test data with a P3 1.13GHz processor.

in (Chen, 2001), the accuracy of ¢ ¤ £ ¦ ¥ £ ¤ was achieved by a Viterbi search program that took about 5 days to supertag the test data.


Based on the local context of R v ' ( P , Three can be the subject of leading.

the supertag of leading is B An, which represents a modifier of a noun.


It is important to note that the accuracy of supertag itself is much lower than that of POS tag while the use of supertags helps to improve the overall performance.

since the accuracy of supertagging is rather lower, there is more room left for improving.


According to the parsing algorithm in Figure 2, the probability of 4 Theoretically, we should arrive at the same dependency structure no matter whether we parse the sentence left to right or right to left.

this is not the case with the approximation algorithm.


Inter-annotator agreement on the frames shown in Table 1 is very high.

the lemmas we considered so far were only moderately ambiguous, and we might see lower figures for frame agreement for highly polysemous FEEs like laufen (to run).


The Prague Treebank reported a disagreement of about 10% for manual thematic role assignment (Žabokrtský, 2000).

in contrast to our study, they also annotated temporal and local modifiers, which are easier to mark than other roles.


We suspect that annotators saw too few instances of these elements to build up a reliable intuition.

the elements may also be inherently difficult to distinguish.


It is unfortunate that annotators use underspecification only infrequently, since it can indicate interesting cases of relatedness between different frames and frame elements.

underspecification may well find its main use during the merging of independent annotations of the same corpus.


Although these and other researchers have suggested that nonverbal behaviors undoubtedly play a role in grounding, previous literature does not characterize their precise role with respect to dialogue state.

a number of studies on these particular nonverbal behaviors do exist.


<Assertion> In both conditions, listeners look at the map most of the time, and sometimes nod.

speakers' nonverbal behavior is very different across conditions.


Moreover, when a listener keeps looking at the speaker, the speaker's next UU is go-ahead only 27% of the time.

when a listener keeps looking at the map, the speaker's next UU is go-ahead 52% of the time (z = -2.049, p<.05) 1 .


If we apply this model to the example in Figure 1, none of the UU have been grounded because the listener has not returned any spoken grounding clues.

our results suggest that considering the role of nonverbal behavior, especially eye-gaze, allows a more fine-grained model of grounding, employing the UU as a unit of grounding.


Consistent with the previous findings (Oviatt et al, 1997), in most cases (85% of time), gestures occurred before the referring expressions were uttered.

in 15% of the cases the speech referring expressions were uttered before the gesture occurred.


In this case, the overall similarity function will return zero.

because of the iterative updating nature of the matching algorithm, the system will still find the most optimal match as a result of the matching process even some constraints are violated.


The speaker first attempts to use constructions from his existing inventory to express whatever he wants to express.

when that fails or is judged unsatisfactory, the speaker may extend his existing repertoire by inventing new constructions.


We assume that X is a substring of Y , i.e., that the source sentence can be obtained by deleting words from Y , so for a fixed observed sentence there are only a finite number of possible source sentences.

the number of source sentences grows exponentially with the length of Y , so exhaustive search is probably infeasible.


Finally, auxiliary trees of the form (β 5 ) generate a reparandum word M i is inserted; the weight of such a tree is The TAG just described is not probabilistic; informally, it does not include the probability costs for generating the source words.

it is easy to modify the TAG so it does include a bigram model that does generate the source words, since each nonterminal encodes the preceding source word.


That approach may do so well because many speech repairs are very short, involving only one or two words Shriberg and Stolcke (1998), so the reparandum, interregnum and repair are all contained in the surrounding word window used as features by the classifier.

the probabilistic model of repairs explored here seems to be most successful in identifying long repairs in which the reparandum and repair are similar enough to be unlikely to have been generated independently.


It is likely that edges recently discovered by the attention shifting procedure are pruned.

the true PCFG probability model is used to prune these edges rather than the approximation used in the FOM.


To produce the word-lattices, each training utterance was processed by the baseline ASR system.

these same utterances are what the acoustic and language models are built from, which leads to better performance on the training utterances than can be expected when the ASR system processes unseen utterances.


Traditional concatenative speech synthesis systems use a number of heuristics to define the target and concatenation costs, essential for the design of the unit selection component.

to these approaches, we introduce a general statistical modeling framework for unit selection inspired by automatic speech recognition.


This would provide improved feedback to the user about the available choices so far, guards against stilted conversations with a fixed number of dialog turns for every interaction, and mitigates against repeated scenarios where user queries return no items.

much effort is then required in configuring the numerous scenarios for users to make sequences of queries in various orders.


Our classifier performs well if the utterance is short and falls into one of the selected categories (86% accuracy on the British data); and it has the advantages of automatic training, domain independence, and the ability to capture a great variety of expressions.

it can be inaccurate when applied to longer utterances, and it is not yet equipped to handle domain-specific assertions, questions, or queries about a transaction.


For instance, already (Smith, 1993) observed that it is safer for beginners to be closely guided by the system, while experienced users like to take the initiative which results in more efficient dialogues in terms of decreased average completion time and a decreased average number of utterances.

being able to decide when to switch from guiding a novice to facilitating an expert requires the system to be able to keep track of the user's expertise level.


As mentioned above, one of the goals of the Cooperativity model is to facilitate more natural interaction by allowing the system to adapt its utterances according to the perceived expertise level.

we also want to validate and assess the usability of the three-level model of user expertise.


This should contribute to a feeling of consistency and dependability.

paris (1988) argued that the user's expertise level does not affect only the amount but the kind of information given to the user.


In particular, the probability distribution over sentences can be derived from the joint probability distribution, but not from the conditional one.

the unbounded nature of the parsing problem means that the individual parameters of the discriminative model are much harder to estimate than those of the generative model.


The estimation process has to guess about the future role of an unbounded number of words, which makes the estimate quite difficult.

the parameters of the generative model only include words which are either already incorporated into the structure, or are the immediate next word to be incorporated.


In  we argued for the use of log-linear parsing models for CCG.

estimating a log-linear model for a widecoverage CCG grammar is very computationally expensive.


To solve this issue, we generally eliminate large sub-structures from the set of features used.

the main reason for using convolution kernels is that we aim to use structural features easily and efficiently.


We have insufficient space to discuss this subject in detail in relation to other convolution kernels.

our proposals can be easily applied to tree kernels (Collins and Duffy, 2001) by using string encoding for trees.


The coreferential chain length of a candidate, or its variants such as occurrence frequency and TFIDF, has been used as a salience factor in some learning-based reference resolution systems (Iida et al., 2003;Mitkov, 1998;Paul et al., 1999;Strube and Muller, 2003).

for an entity, the coreferential length only reflects its global salience in the whole text(s), instead of the local salience in a discourse segment which is nevertheless more informative for pronoun resolution.


The model is appealing in that it can potentially overcome the limitation of mention-pair model in which dependency among mentions other than the two in question is ignored.

models in (McCallum and Wellner, 2003) compute directly the probability of an entity configuration conditioned on mentions, and it is not clear how the models can be factored to do the incremental search, as it is impractical to enumerate all possible entities even for documents with a moderate number of mentions.


For training coreference classifiers and locally-optimized anaphoricity models, we use both RIPPER and MaxEnt as the underlying learning algorithms.

for training globally-optimized anaphoricity models, RIPPER is always used in conjunction with Method 1 and Max-Ent with Method 2, as described in Section 2.2.


To illustrate the importance of contextual information in transliteration, let's take name /Minahan/ as an example, the correct segmentation should be /Mi-na-han/, to be transliterated as 米-纳-汉 (Pinyin: Mi-Na-Han).

a possible segmentation /Min-ah-an/ could lead to an undesirable syllabication of 明-阿-安 (Pinyin: Min-A-An).


In order words, on average, for each English unit, we have 1.53 = 5,640/3,683 Chinese correspondences.

for each Chinese unit, we have 15.1 = 5,640/374 English back-transliteration units!


The segmentation step correctly segmented the romanji to "matsu-da".

in the Unihan database, 14 At this rate, checking the 21 million combinations remaining after filtering with bigrams using the Web (without the corpus filtering step) would take more than a year.


From a biological point of view, there are two problems with such approaches: 1) the meaning of the extracted events will depend strongly on the selectional restrictions and 2) the same meaning can be expressed using a number of different verbs.

and alike (Friedman et al., 2001), we instead set out to handle only one specific biological problem and, in return, extract the related events with their whole range of syntactic variations.


to the number of standard deviations the score differs from the mean of the scores of the unattributed samples.

this renormalization only makes sense in the situation that we have a fixed set of authors who each produced one text for each topic.


The combination of the best two individual systems leads to an FAR FRR=0 of 10.3%, a solid improvement over lexical features by themselves.

the best individual systems are not necessarily the best combiners.


Above, we focused on the authorship verification task, since it is the harder problem, given that the potential group of authors is unknown.

as mentioned in Section 2, previous work with this data has focused on the authorship recognition problem, to be exact on selecting the correct author out of two potential authors.


Formal aspects of a summary (or report), such as legibility, grammatical correctness, informativeness, etc., can only be evaluated manually.

automatic evaluation metrics can play a useful role in the evaluation of how well the information from the original sources is preserved (Mani, 2001).


While the ROUGE measure remains stable (0.53 versus 0.54), the key concept similarity is much worse with IE topics (0.52 versus 0.77).

all baselines improve, and some of them (SentenceSim precision and perplexity) give better results than both ROUGE and NICOS.


4 By reducing the tag-set considered by the parsing model, we reduce the search space and increase the speed.

the simple tagger used to narrow the search also introduces tagging error.


Further improvement is seen with the combination of acoustic, parsing, and trigram scores (α ¡ 1¦ 16 β ¡ 1).

the combination of the parsing model (trained on 1M words) with the lattice trigram (trained on 40M words) resulted in a higher WER than the lattice trigram alone.


This translation clarified the precise relationship between these two related formalisms, and made the powerful meta-theory of dominance constraints accessible to MRS. Their goal was to also make the large grammars for MRS and the efficient constraint solvers for dominance constraints available to the other formalism.

niehren and Thater made three technical assumptions: 1. that EP-conjunction can be resolved in a preprocessing step; 2. that the qeq relation in MRS is simply dominance; 3. and (most importantly) that all linguistically correct and relevant MRS expressions belong to a certain class of constraints called nets.


In unsupervised models, these coefficients are assumed to be known.

when labeled documents are available, it may be advantageous to estimate them.


As with document-level polarity classification, we could perform subjectivity detection on individual sentences by applying a standard classification algorithm on each sentence in isolation.

modeling proximity relationships between sentences would enable us to leverage coherence: text spans occurring near each other (within discourse boundaries) may share the same subjectivity status, other things being equal (Wiebe, 1994).


Assuming that one had an accurate WSD system then one could obtain frequency counts for senses and rank them with these counts.

the most accurate WSD systems are those which require manually sense tagged data in the first place, and their accuracy depends on the quantity of training examples (Yarowsky and Florian, 2002) available.


Note that in the absence of labeled training data, two of the random variables E ¡ and ¢ £ are observed, while the sense variable 8 is not.

we can derive the possible values for our sense labels from WordNet, which gives us the possible senses for each English word E ¡ .


For the Sense Model, these probabilities form the initial priors over the senses, while all English (and Spanish) words belonging to a sense are initially assumed to be equally likely.

initialization of the Concept Model using the same knowledge is trickier.


This may be counter-intuitive since Concept Model involves an extra concept variable.

the dissociation of Spanish and English senses can significantly reduce the parameter space.


Second, Hownet and Rocling sometimes use adjectives or non-words as category glosses, such as animate and LandVehicle etc., which have no WordNet nominal hypernyms at all.

those adjectives or non-words usually have straightforward meanings and can be easily reassigned to an appropriate WordNet category.


Correlation between the automatic tagging precision and STE is expected to be high if SALAAM has good quality translations and good quality alignments.

this correlation is a low 9 4 A C B E 8 8 .


We observe cases with a low PerpDiff such as holiday (PerpDiff of A C BA ¡ ), yet the PR is a low .

items such as art have a relatively high PerpDiff of .


Accordingly, in this case, STE coupled with the noisy tagging could have resulted in the low PR.

for circuit, the STE value for its respective condition is a high  for the manually annotated data.


It seems that the f-structure-based approach is more abstract (99 LDD path types against approximately 9,000 tree-fragment types in (Johnson, 2002)) and fine-grained in its use of lexical information (subcat frames).

to Johnson's approach, our LDD resolution algorithm is not biased.


Earlier HPSG work (Tateisi et al., 1998) is based on independently constructed hand-crafted XTAG resources.

we acquire our resources from treebanks and achieve substantially wider coverage.


Knowledge of these hidden relationships is in turn essential to semantic interpretation of the kind practiced in the semantic parsing (Gildea and Jurafsky, 2002) and QA (Pasca and Harabagiu, 2001) literatures.

work in statistical parsing has for the most part put these needs aside, being content to recover surface context-free (CF) phrase structure trees.


2 NEGRA's original annotation is as dependency trees with phrasal nodes, crossing branches, and no empty elements.

the distribution includes a context-free version produced algorithmically by recursively remapping discontinuous parts of nodes upward into higher phrases and marking their sites of origin.


Such low value depends on the fact that different arguments tend to appear in different structures.

if two structures differ only for a few nodes (especially terminals or near terminal nodes) the similarity remains quite high.


Finally, from the above observations follows that the PAF representation may be used with PAK to classify arguments.

sCF lacks important information, thus, alone it may be used only to classify verbs in syntactic categories.


In fact, in PropBank arguments are annotated consistently with syntactic alternations (see the Annotation guidelines for Prop-Bank at www.cis.upenn.edu/∼ace).

frameNet roles represent the final semantic product and they are assigned according to semantic considerations rather than syntactic aspects.


Although consistently with prior research we find that the combined feature sets usually outperform the speech-only feature sets, the combined feature sets frequently perform worse than the lexical-only feature sets.

we will see in Section 6 that combining knowledge sources does improve prediction performance in human-human dialogues.


Moreover, it is no longer the case that every feature  Table 9: %Corr., NPN Consensus, MAJ=48.35% set performs as well as or better than their baselines 6 ; within the "-id" sets, NnN "sp" and EnE "lex" perform significantly worse than their baselines.

again we see that the "+id" sets do consistently better than the "-id" sets and moreover always outperform the baselines.


Every feature set performs significantly better than their baselines.

unlike the computerhuman data, we don't see the "+id" sets performing better than the "-id" sets; rather, both sets perform about the same.


attempt to derive relative subcategorization frequency for individual predicates".

the system of (Briscoe and Carroll, 1997) distinguishes 163 verbal subcategorisation classes by means of a statistical shallow parser, a classifier of subcategorisation classes, and a priori estimates of the probability that any verb will be a member of those classes.


(2004) found that the majority of the recorded transitions in the configuration of Centering used in this study are nocbs.

we also explained in section 2.3 that what really matters when trying to determine whether a text might have been generated only paying attention to Centering constraints is the extent to which it would be possible to 'improve' upon the ordering chosen in that text, given the information that the text structuring algorithm had to convey.


Subsequent work on referring expression generation has expanded the logical framework to allow reference by negation (the dog that is not black) and references to multiple entities (the brown or black dogs) (van Deemter, 2002), explored different search algorithms for finding the minimal description (e.g., Horacek (2003)) and offered different representation frameworks like graph theory (Krahmer et al., 2003) as alternatives to AVMs.

all these approaches are based on very similar formalisations of the problem, and all make the following assumptions: 1.


For attributes, we defined DQ = CQ − SQ.

as the linguistic realisation of a relation is a phrase and not a word, we would like to normalise the discriminating power of a relation with the length of its linguistic realisation.


We define the parsability of a word R(w) as the ratio of the number of times the word occurs in a sentence with a successful parse (C(w|OK)) and the total number of sentences that this word occurs in (C(w)): Thus, if a word only occurs in sentences that cannot be parsed successfully, the parsability of that word is 0.

if a word only occurs in sentences with a successful parse, its parsability is 1.


: word break, named entity recognition and morphological analysis) in a unified approach.

it is difficult to develop a training corpus where new words are annotated because "we usually do not know what we don't know".


Whenever we deploy the segmenter for any application, we need to customize the output of the segmenter according to an application-specific standard, which is not always explicitly defined.

it is often implicitly defined in a given amount of application data (called adaptation data) from which the specific standard can be partially learned.


A number of studies are related to the work we presented, most specifically work on parallel-text based "information projection" for parsing (Hwa et al., 2002), but also grammar induction work based on constituent/distituent information (Klein and Manning, 2002) and (language-internal) alignmentbased learning (van Zaanen, 2000).

to our knowledge the specific way of bringing these aspects together is new.


In those experiments, the model above was trained on over 30M words of raw newswire, using EM in an entirely unsupervised fashion, and at great computational cost.

as shown in figure 3, the resulting parser predicted dependencies at below chance level (measured by choosing a random dependency structure).


Clark (2001) and Klein and Manning (2002) show that this approach can be successfully used for discovering syntactic constituents as well.

as one might expect, it is easier to cluster word sequences (or word class sequences) than to tell how to put them together into trees.


DA happily obtained a 10% reduction in tag error rate on training data, and an 11% reduction on test data.

it did not manage to improve likelihood over EM.


It turned out that the implementation was fast, but many operations wasted a lot of memory as their resulting transducer had been fully expanded in memory.

we plan to also make this initial version publically available.


The disadvantage of using pipes is that automata must be serialized and get fully expanded by the next executable in chain.

an advantage of multiple executables is that memory does not get fragmented through the interaction of different algorithms.


If one equates optimum parameter estimation with finding the global maximum for the likelihood of the training data, then this result would seem to show no improvement is possible.

in virtually every application of statistical techniques in natural-language processing, maximizing the likelihood of the training data causes overfitting, resulting in lower task performance than some other estimates for the model parameters.


In comparison to parallel corpora, ie corpora which are mutual translations, comparable corpora have not received much attention from the research community, and very few methods have been proposed to extract bilingual lexicons from such corpora.

except for those found in translation services or in a few international organisations, which, by essence, produce parallel documentations, most existing multilingual corpora are not parallel, but comparable.


Such a combination could involve Fisher kernels with different latent classes in a first step, and a final combination of the different methods.

the results we obtained so far suggest that the rank of the candidates is an important feature.


A lot of effort has been spent on constructing translation lexicons from domain-specific corpora in an automatic way (Melamed, 2000;Smadja et al., 1996;Kupiec, 1993).

such methods encounter two fundamental problems: translation of regional variations and the lack of up-to-date and high-lexical-coverage corpus source, which are worthy of further investigation.


Only 100 snippets retrieved might not balance or be sufficient for translation extraction.

the inclusion rates for the three regions were close in the top-5.